CN111260487A - Risk control method and device for vehicle insurance claim settlement - Google Patents
Risk control method and device for vehicle insurance claim settlement Download PDFInfo
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Abstract
The invention is suitable for the technical field of computers, and provides a risk control method and a risk control device for vehicle insurance claims, wherein the method comprises the following steps: acquiring relevant data information of a vehicle to be predicted and external factor data corresponding to the relevant data information; processing the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted by adopting a residual attention network, a self-encoder and a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted; determining a subsequent vehicle insurance claim settlement process according to the vehicle damage evaluation index of the vehicle to be predicted, and ensuring the accuracy of an evaluation result based on the use of an artificial intelligence simulation vehicle damage evaluation method; the unreasonable risk of subsequent claim settlement programs is obviously reduced according to the accurate evaluation result; the adverse effect or influence caused by cheating insurance or manual evaluation is reduced; the benefits of insurance companies and vehicle owners involved in the case are guaranteed.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a risk control method and device for vehicle insurance claim settlement.
Background
In recent years, the automobile holding capacity of each city rapidly increases year by year, the automobile insurance market is more complex, the development space is further expanded, meanwhile, the automobile insurance reform is also gradually promoted, and the accuracy of automobile insurance result evaluation and claim settlement is challenged. Through relevant data investigation, it is known that in recent years, certain achievements are achieved on the accuracy of vehicle insurance result evaluation and claim settlement on the whole in China, but in practical application, more problems still exist, the market economic environment development cannot be effectively adapted to, a larger promotion space also exists, and along with the development of scientific technology, a scientific basis is provided for further improving the accuracy of vehicle insurance result evaluation and claim settlement. In the prior art, the traditional method or the improved traditional method is mainly used for improving or accelerating the efficiency of vehicle insurance claim settlement or the claim settlement process, or whether a user involved in case insurance carries out further verification on cheating insurance, but there is a new technology or a new method for carrying out prediction on the evaluation result of vehicle damage by an insurance company, carrying out further verification on the evaluation result determined by the traditional human evaluation method of vehicle damage by the insurance company or the improved traditional human evaluation method, namely, evaluating the prediction or further verifying the evaluation result of vehicle damage, and further, the phenomenon that the benefits of the vehicle insurance company or the owner of the case insurance are damaged due to inaccurate vehicle damage assessment or unreasonable claim settlement is existed.
Disclosure of Invention
In view of this, embodiments of the present invention provide a risk control method and apparatus for vehicle insurance claims, a terminal device, and a computer-readable storage medium, so as to solve the problem in the prior art that a subsequent vehicle insurance claim is not reasonable due to inaccurate vehicle damage assessment result.
In a first aspect of the embodiments of the present invention, a risk control method for vehicle insurance claims is provided, including:
acquiring relevant data information of a vehicle to be predicted and external factor data corresponding to the relevant data information;
processing the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted by adopting a residual attention network, a self-encoder and a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted;
and determining a subsequent vehicle insurance claim settlement process according to the vehicle damage evaluation index of the vehicle to be predicted.
In a second aspect of the embodiments of the present invention, there is provided a risk control device for vehicle insurance claim settlement, including:
the data acquisition module is used for acquiring relevant data information of a vehicle to be predicted and external factor data corresponding to the relevant data information;
the evaluation module is used for processing the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted by adopting a residual attention network, a self-encoder and a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted;
and the determining module is used for determining a subsequent vehicle insurance claim settlement process according to the vehicle damage evaluation index of the vehicle to be predicted.
In a third aspect of the embodiments of the present invention, there is provided a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the risk control method for vehicle insurance claims when executing the computer program.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where a computer program is stored, and the computer program, when executed by a processor, implements the steps of the risk control method for vehicle insurance claims.
The risk control method for vehicle insurance claim settlement provided by the embodiment of the invention has the beneficial effects that at least: the method for evaluating the vehicle damage is simulated based on artificial intelligence, so that the accuracy of an evaluation result is ensured; the unreasonable risk of subsequent claim settlement programs is obviously reduced according to the accurate evaluation result; the adverse effect or influence caused by cheating insurance or manual evaluation is reduced; the benefits of insurance companies and vehicle owners involved in the case are guaranteed.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a first schematic flow chart illustrating an implementation of a risk control method for car insurance claim settlement according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating an implementation process of obtaining a vehicle damage assessment index of the vehicle to be predicted in the risk control method for vehicle insurance claim settlement provided by the embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating an implementation process of determining whether a vehicle damage assessment index of the vehicle to be predicted meets a preset condition in the risk control method for vehicle insurance claim settlement according to the embodiment of the present invention;
fig. 4 is a schematic flow chart illustrating an implementation of a risk control method for car insurance claim settlement according to an embodiment of the present invention;
fig. 5 is a schematic flow chart illustrating an implementation process of training an initial residual attention network, an initial self-encoder, and an initial neural network in the risk control method for vehicle insurance claims provided in the embodiment of the present invention;
fig. 6 is a schematic flow chart illustrating an implementation process of determining a residual attention network, a self-encoder and a neural network which meet preset requirements for predicting a vehicle damage assessment index in the risk control method for vehicle insurance claims provided in the embodiment of the present invention;
FIG. 7 is a first schematic diagram of a risk control apparatus for vehicle insurance claim settlement according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of an evaluation module in the risk control device for vehicle insurance claims provided in the embodiment of the present invention;
FIG. 9 is a schematic diagram of a determination module in the risk control device for vehicle insurance claims settlement according to the embodiment of the present invention;
FIG. 10 is a second schematic diagram of a risk control device for vehicle insurance claim settlement according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a training module in the risk control device for vehicle insurance claims provided in the embodiment of the present invention;
fig. 12 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
For the loss assessment method in the vehicle insurance, the prior art mainly depends on manual work to evaluate the accident result, and the prior art has the following defects: the professional abilities or experience levels of the evaluators are different, the evaluation result is large in difference and low in accuracy; the evaluation result has certain subjectivity and experience; the evaluation method is single, and further verification means or methods are lacked after the result is determined; the evaluation efficiency is improved, but the accuracy is poor, so that the accuracy of the evaluation of the subsequent claim settlement result is influenced, the risk of claim settlement is increased, and the benefits of insurance companies or clients involved in case accidents are damaged.
In view of this, embodiments of the present invention provide a risk control method and apparatus for vehicle insurance claims, a terminal device, and a computer-readable storage medium, so as to solve the problem in the prior art that a subsequent vehicle insurance claim is not reasonable due to inaccurate vehicle damage assessment result. The method can be directly used for predicting the vehicle damage assessment result, and can also be used as a means for further verification after the assessment result is determined based on the traditional manpower assessment method.
The invention is based on technologies such as big data, artificial intelligence and the like, utilizes the acquired data information to trace the original scene, completes the analysis of the data information, thereby carrying out risk assessment according to the analysis result, further judging whether insurance companies need to carry out claim settlement or not, and further providing wind control support for the insurance companies and the clients involved in the case. The method specifically comprises the following steps: collecting related data information by using related technology equipment; the analysis of the data information is completed based on big data and artificial intelligence technology, thereby significantly reducing the unreasonable or inaccurate risk of vehicle insurance claims settlement.
Referring to fig. 1, a first implementation flow diagram of a risk control method for car insurance claims provided in the embodiment of the present invention is shown, where the method may include:
step S10: and acquiring relevant data information of the vehicle to be predicted and external factor data corresponding to the relevant data information.
Acquiring relevant data information of the vehicle to be predicted in a mode of extracting the characteristics of relevant original image information of the vehicle for the image information to acquire characteristic information; and performing feature recognition according to the feature information to determine corresponding text information.
In this embodiment, the vehicle to be predicted may be understood as a related event vehicle, and the event vehicle may be one, two or more vehicles, and the vehicle to be predicted may take a picture of the event vehicle or may take a picture of all related information of the event vehicle or may present the scene in a picture format, and in this embodiment, may be regarded as original image information about the event vehicle. That is, the original image information may be information in a picture format. Subsequently, image recognition can be performed based on the original image information, so that possible text contents (driver's license, driving license, etc.) in the image can be converted into text information, and further structured. Of course, the related data information is not limited to the picture information, and the related information in various forms may be acquired by, for example, a conventional device or technology, such as a car recorder, a road camera, and the like.
In order to improve the accuracy of image recognition in this embodiment, it is preferable that the original image information is subjected to corresponding preprocessing, so that the original image information is more standard and is easy to recognize. The pretreatment typically includes: noise reduction, distortion correction, color adjustment, and the like. This is not limited in this embodiment, and any relevant image preprocessing technique can be incorporated in this embodiment.
In this conversion process, it is necessary to extract feature information from the text image information, specifically, feature information on the image level of the "text content". In this embodiment, the specific feature extraction method is not limited. Any image recognition technology that can achieve the same or similar functions can be incorporated into the overall solution of the present embodiment.
And acquiring external factor data of the vehicle to be predicted in a mode of observing the terrain condition and the traffic condition of a place of affairs and acquiring the weather condition by using a search engine or media information.
It should be understood that the observation method, the search engine or the media information listed in the present embodiment are only a preferred method for obtaining the relevant information, and any method capable of quickly and conveniently obtaining the above information may be combined in the present embodiment.
Acquiring historical data in a vehicle insurance database; and according to a preset rule, extracting a similar case of the vehicle to be predicted from the historical data, wherein the data information of the similar case at least comprises the relevant data information of the vehicle of the similar case and the external factor data of the similar case.
The car insurance database can be established by the insurance company, or can be established by a third party or the same bank, and is not limited here; the historical data in the vehicle insurance database may be all the information about the case of the vehicle accident and the case related information accumulated over many years or even several decades, and is not limited herein.
In this embodiment, the preset rule needs to specifically preset the relevant rule according to factors such as the car insurance database, the historical data, and the used artificial intelligence processing model, wherein important information includes relevant data information of the vehicles in the similar cases and external factor data of the similar cases, but it should be understood that the preset rule is not limited to the relevant data information of the vehicles in the similar cases and the external factor data of the similar cases.
The relevant data information of the vehicle comprises basic information of the vehicle and/or damage information of the vehicle and/or scattered object information and/or personnel information.
The external factor data includes weather conditions and/or conditions around the venue and/or traffic conditions at the venue.
In this embodiment, the data information related to the vehicle and the external factor data may not be limited to the above contents, and may also or may include more detailed contents, such as: the involved vehicles can comprise a single party, two parties or multiple parties; license plate number, vehicle color, damage degree and the like of each involved vehicle; weather conditions: including weather conditions on the day of the incident, weather conditions at the time of the incident, and the like; situation around the incident: including mountains, hills, forests, road conditions, vision conditions, etc.; traffic conditions at the time of the incident: the nature of the road (high speed, provincial road, etc.) and traffic flow (congested, normal, sparse, etc.), etc.; scattered object information: including front and rear bumpers, headlights, glass, etc.; personnel attribute information: including case-involved personnel, case-involved personnel information, injured people, injured condition, injured personnel information and the like. Of course, with the development of scientific technology and the development of related analysis models, it is possible to add or subtract related information on the basis of the above information, and this is not limited herein.
Referring to fig. 1, further, after obtaining the relevant data information of the vehicle to be predicted and the external factor data corresponding to the relevant data information, the following steps may be performed:
step S30: and processing the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted by adopting a residual attention network, a self-encoder and a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted.
Further, in order to obtain the vehicle loss evaluation index of the vehicle to be predicted, a residual attention network, an autoencoder and a neural network are adopted to process the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted. Referring to fig. 2, it is a schematic flow chart of an implementation of obtaining the vehicle damage assessment index of the vehicle to be predicted in the risk control method for vehicle insurance claims provided in the embodiment of the present invention, and in this embodiment, a manner of obtaining the vehicle damage assessment index of the vehicle to be predicted may include the following steps:
step S301: and processing the relevant data information of the vehicle to be predicted by adopting a residual attention network to obtain relevant data characteristics.
It should be understood that, for a plurality of different information characteristics, a plurality of residual attention networks may be respectively used to respectively perform specific data analysis on the different information characteristics, and is not limited herein.
Step S302: and processing the external factor data of the vehicle to be predicted by adopting an auto-encoder to obtain the external factor characteristics.
Step S303: and acquiring a combined feature according to the related data feature and the external factor feature.
Step S304: and processing the combined characteristics by adopting a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted.
The evaluation index in this embodiment may be a percentage, or may be a specific natural number, such as 7, or 89, and is not limited herein.
Referring to fig. 1, further, after obtaining the vehicle damage assessment index of the vehicle to be predicted, the following steps may be performed:
step S50: and determining a subsequent vehicle insurance claim settlement process according to the vehicle damage evaluation index of the vehicle to be predicted.
Further, in order to determine the subsequent vehicle insurance claim settlement process, it is required to determine whether the vehicle damage assessment index of the vehicle to be predicted meets a preset condition. Referring to fig. 3, a schematic flow chart of an implementation of the method for risk control of vehicle insurance claim settlement according to the embodiment of the present invention to determine whether the vehicle damage assessment index of the vehicle to be predicted meets a preset condition, where in this embodiment, a manner of determining a subsequent vehicle insurance claim settlement process may include the following steps:
step S501: and judging whether the vehicle loss evaluation index of the vehicle to be predicted meets a preset condition.
It should be understood that the preset condition in this embodiment may be preset by referring to the "perfect" case in the above-mentioned historical database, or the result of the "ideal" case, or by referring to the experience of the expert or advanced vehicle impairment personnel, and of course, other important information may also be referred to as a reference for setting the preset condition, which is not limited herein.
If the vehicle loss evaluation index of the vehicle to be predicted meets the preset condition, performing step S502: normal claim settlement procedure.
In the embodiment, on the basis of meeting the preset conditions, the inaccurate determination of the vehicle accident damage assessment of the traditional method is obviously reduced, and the subsequent claim settlement stage is reasonable on the basis, so that the risk caused by unreasonable and inaccurate damage assessment results is reduced, and the benefits of insurance companies and clients are maintained.
If the vehicle loss evaluation index of the vehicle to be predicted does not meet the preset condition, performing step S503: and returning the training step of the initial residual attention network and the initial self-encoder by adopting the training data, and/or performing artificial auxiliary evaluation.
If the vehicle loss evaluation index of the vehicle to be predicted is poor compared with the preset threshold value or the preset condition and cannot be used as a reference, retraining can be performed on the model or artificial auxiliary evaluation can be referred to, and the evaluation result is more accurate.
Please refer to fig. 4, which is a schematic flow chart illustrating an implementation process of the risk control method for vehicle insurance claims according to the embodiment of the present invention. The method for processing the relevant data information of the vehicle to be predicted by adopting the residual attention network comprises the following steps before the step of acquiring the relevant data characteristics:
step S20: and training the initial residual attention network, the initial self-encoder and the initial neural network to obtain the residual attention network, the self-encoder and the neural network which meet preset requirements.
Further, in order to obtain a residual attention network, an auto-encoder and a neural network which meet preset requirements, the initial residual attention network, the initial auto-encoder and the initial neural network need to be trained. Please refer to fig. 5, which is a schematic diagram illustrating an implementation process of training an initial residual attention network, an initial self-encoder, and an initial neural network in the risk control method for vehicle insurance claims provided in the embodiment of the present invention, wherein in this embodiment, one way of obtaining the residual attention network, the self-encoder, and the neural network that satisfy the preset requirements may include the following steps:
step S201: and segmenting the historical data to obtain training data and test data.
For example, the segmentation ratio of the training data and the test data may be 2: 1. It should be understood that the division ratio can be other ratios, and needs to be determined according to specific cases, and is not limited herein.
Step S202: and training the initial residual attention network and the initial self-encoder by using the training data to obtain a trained residual attention network, a trained self-encoder and a training combination feature.
Step S203: and training the initial neural network by adopting the training combination characteristics to obtain a trained neural network.
Step S204: and testing the trained residual attention network, the trained self-encoder and the trained neural network by adopting the test data so as to determine that the residual attention network, the self-encoder and the neural network which meet the preset requirements are used for predicting the vehicle loss assessment index.
Further, in order to determine that the residual attention network, the self-encoder and the neural network which meet preset requirements are used for predicting the vehicle loss assessment index, the trained residual attention network, the trained self-encoder and the trained neural network need to be tested by using the test data. Please refer to fig. 6, which is a schematic flow chart illustrating an implementation process of determining that the residual attention network, the self-encoder, and the neural network meeting the preset requirements are used for predicting the vehicle damage assessment index in the risk control method for vehicle insurance claims provided in the embodiment of the present invention, in this embodiment, a manner of determining that the residual attention network, the self-encoder, and the neural network meeting the preset requirements are used for predicting the vehicle damage assessment index may include the following steps:
step S2041: and inputting the test data into the trained residual error attention network and the trained self-encoder to obtain test combination characteristics.
Step S2042: and inputting the test combination characteristics into the trained neural network to obtain a test prediction result.
Step S2043: obtaining a test index according to the test prediction result and the test data, wherein the root mean square error obtaining mode of the test index is as follows:
wherein, yiTest values characterizing the test data in the similar cases,
and characterizing the predicted value of the test data in the similar cases.
Step S2044: and judging whether the test index meets a preset requirement.
If the test index meets the preset requirement, step S2045: determining that the trained residual attention network, the trained self-encoder, and the trained neural network are a residual attention network, a self-encoder, and a neural network, respectively, for prediction of a vehicle loss assessment index.
If the test index does not meet the preset requirement, step S2046: and returning to the step of training the initial residual attention network and the initial self-encoder by using the training data.
It should be understood that the above-mentioned letters and/or symbols are only used for clearly explaining the meaning of specific parameters of the device or steps, and other letters or symbols can be used for representing the device or steps, which is not limited herein.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The risk control method for vehicle insurance claim settlement provided by the embodiment of the invention has the beneficial effects that at least: the method for evaluating the vehicle damage is simulated based on artificial intelligence, so that the accuracy of an evaluation result is ensured; the unreasonable risk of subsequent claim settlement programs is obviously reduced according to the accurate evaluation result; the adverse effect or influence caused by cheating insurance or manual evaluation is reduced; the benefits of insurance companies and vehicle owners involved in the case are guaranteed.
Fig. 7 is a first schematic diagram of the risk control device for vehicle insurance claim settlement provided in the embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown.
Referring to fig. 7, a risk control device for vehicle insurance claims includes a data acquisition module 61, an evaluation module 63, and a determination module 65. The data acquisition module 61 is used for acquiring relevant data information of a vehicle to be predicted and external factor data corresponding to the relevant data information; the evaluation module 63 is configured to process the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted by using a residual attention network, a self-encoder and a neural network, and obtain a vehicle loss evaluation index of the vehicle to be predicted; the determining module 65 is configured to determine a subsequent vehicle insurance claim settlement process according to the vehicle damage assessment index of the vehicle to be predicted.
Referring to fig. 8, the evaluation module 63 further includes a first feature obtaining unit 631, a second feature obtaining unit 632, a combined feature obtaining unit 633 and an evaluation unit 634. The first feature obtaining unit 631 is configured to process relevant data information of the vehicle to be predicted by using a residual attention network, and obtain relevant data features; the second characteristic obtaining unit 632 is configured to process the external factor data of the vehicle to be predicted by using a self-encoder to obtain an external factor characteristic; the combined feature obtaining unit 633 is configured to obtain a combined feature according to the relevant data feature and the external factor feature; the evaluation unit 634 is configured to process the combined features by using a neural network, and obtain a vehicle loss evaluation index of the vehicle to be predicted.
Referring to fig. 9, further, the determination module 65 includes a judgment unit 651, a claim settlement unit 652 and a return unit 653. The judging unit 651 is configured to judge whether the vehicle loss evaluation index of the vehicle to be predicted meets a preset condition; the claim settlement unit 652 is configured to perform a normal claim settlement procedure if the vehicle damage assessment index of the vehicle to be predicted meets a preset condition; the returning unit 653 is configured to return the training step performed on the initial residual attention network and the initial self-encoder by using the training data, and/or perform manual auxiliary evaluation, if the vehicle loss evaluation index of the vehicle to be predicted does not meet the preset condition.
Further, please refer to fig. 10, which is a second schematic diagram of the risk control apparatus for vehicle insurance claim settlement according to the embodiment of the present invention. The risk control device for vehicle insurance claims settlement further includes a training module 62, configured to train the initial residual attention network, the initial self-encoder, and the initial neural network to obtain a residual attention network, a self-encoder, and a neural network that meet preset requirements.
Referring to fig. 11, the training module 62 includes a data obtaining unit 621, a first training unit 622, a second training unit 623, and a testing unit 624. The data acquisition unit 621 is configured to segment the historical data to acquire training data and test data; a first training unit 622, configured to train the initial residual attention network and the initial self-encoder with the training data to obtain a trained residual attention network, a trained self-encoder, and a training combination feature; the second training unit 623 is configured to train the initial neural network by using the training combination feature to obtain a trained neural network; the testing unit 624 is configured to use the test data to test the trained residual attention network, the trained self-encoder, and the trained neural network, so as to determine that the residual attention network, the self-encoder, and the neural network that meet preset requirements are used for predicting the vehicle damage assessment index.
Fig. 12 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 12, the terminal device 7 includes a memory 71, a processor 70, and a computer program 72 stored in the memory 71 and executable on the processor 70, and when the processor 70 executes the computer program 72, the steps of the risk control method for car insurance claims, such as steps S10 to S50 shown in fig. 1-6, are implemented.
The terminal device 7 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, the processor 70 and the memory 71. It will be appreciated by those skilled in the art that fig. 12 is merely an example of the terminal device 7 and does not constitute a limitation of the terminal device 7 and may comprise more or less components than those shown, or some components may be combined, or different components, for example the terminal device may further comprise input output devices, network access devices, buses, etc.
The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the terminal device 7, such as a hard disk or a memory of the terminal device 7. The memory 71 may also be an external storage device of the terminal device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the terminal device 7. The memory 71 is used for storing the computer program and other programs and data required by the terminal device. The memory 71 may also be used to temporarily store data that has been output or is to be output.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Specifically, the present application further provides a computer-readable storage medium, which may be a computer-readable storage medium contained in the memory in the foregoing embodiments; or it may be a separate computer-readable storage medium not incorporated into the terminal device. The computer readable storage medium stores one or more computer programs:
a computer-readable storage medium comprising a computer program stored thereon which, when executed by a processor, performs the steps of the risk control method of car insurance claims.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A risk control method for vehicle insurance claims, characterized by comprising:
acquiring relevant data information of a vehicle to be predicted and external factor data corresponding to the relevant data information;
processing the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted by adopting a residual attention network, a self-encoder and a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted;
and determining a subsequent vehicle insurance claim settlement process according to the vehicle damage evaluation index of the vehicle to be predicted.
2. The risk control method of a car insurance claim, according to claim 1, comprising:
acquiring relevant data information of the vehicle to be predicted in a mode of extracting the characteristics of relevant original image information of the vehicle for the image information to acquire characteristic information;
performing feature recognition according to the feature information to determine corresponding text information;
and acquiring external factor data of the vehicle to be predicted in a mode of observing the terrain condition and the traffic condition of a place of affairs and acquiring the weather condition by using a search engine or media information.
3. The risk control method of a vehicle insurance claim, according to claim 1, wherein the step of obtaining the relevant data information of the vehicle to be predicted and the external factor data corresponding to the relevant data information further comprises:
acquiring historical data in a vehicle insurance database;
extracting similar cases of the vehicle to be predicted from the historical data according to preset rules, wherein the data information of the similar cases at least comprises the relevant data information of the vehicle of the similar cases and the external factor data of the similar cases;
the relevant data information of the vehicle comprises basic information of the vehicle and/or damage information of the vehicle and/or scattered object information and/or personnel information;
the external factor data includes weather conditions and/or conditions around the venue and/or traffic conditions at the venue.
4. The risk control method for vehicle insurance claims according to claim 1, wherein the processing the data information related to the vehicle to be predicted and the external factor data of the vehicle to be predicted by using a residual attention network, a self-encoder and a neural network to obtain the vehicle damage assessment index of the vehicle to be predicted comprises:
processing the relevant data information of the vehicle to be predicted by adopting a residual attention network to obtain relevant data characteristics;
processing external factor data of the vehicle to be predicted by adopting an auto-encoder to obtain external factor characteristics;
acquiring a combined feature according to the related data feature and the external factor feature;
and processing the combined characteristics by adopting a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted.
5. The risk control method of a vehicle insurance claim, according to claim 4, wherein before the step of processing the relevant data information of the vehicle to be predicted by using the residual attention network and obtaining the relevant data features, the method further comprises:
training an initial residual attention network, an initial self-encoder and an initial neural network to obtain the residual attention network, the self-encoder and the neural network which meet preset requirements, wherein the training comprises the following steps:
segmenting the historical data to obtain training data and test data;
training the initial residual attention network and the initial self-encoder by using the training data to obtain a trained residual attention network, a trained self-encoder and training combination characteristics;
training the initial neural network by adopting the training combination characteristics to obtain a trained neural network;
and testing the trained residual attention network, the trained self-encoder and the trained neural network by adopting the test data so as to determine that the residual attention network, the self-encoder and the neural network which meet the preset requirements are used for predicting the vehicle loss assessment index.
6. The risk control method of a vehicle insurance claim, wherein the testing the trained residual attention network, the trained self-encoder, and the trained neural network using the test data to determine that the residual attention network, the self-encoder, and the neural network that meet preset requirements are used for predicting the vehicle damage assessment index comprises:
inputting the test data into the trained residual attention network and the trained self-encoder to obtain a test combination feature;
inputting the test combination characteristics into the trained neural network to obtain a test prediction result;
obtaining a test index according to the test prediction result and the test data, wherein the root mean square error obtaining mode of the test index is as follows:
wherein, yiTest values characterizing the test data in the similar cases,
representing the predicted value of the test data in the similar case;
judging whether the test index meets a preset requirement or not;
if the test index meets a preset requirement, determining that the trained residual attention network, the trained self-encoder and the trained neural network are respectively a residual attention network, a self-encoder and a neural network for predicting the vehicle loss assessment index;
and if the test index does not meet the preset requirement, returning to the step of training the initial residual error attention network and the initial self-encoder by adopting the training data.
7. The risk control method for vehicle insurance claims according to any one of claims 1 to 6, wherein the determining the subsequent vehicle insurance claim process according to the vehicle damage assessment index of the vehicle to be predicted comprises:
judging whether the vehicle loss evaluation index of the vehicle to be predicted meets a preset condition or not;
if the vehicle damage evaluation index of the vehicle to be predicted meets a preset condition, performing a normal claim settlement program;
and if the vehicle loss evaluation index of the vehicle to be predicted does not meet the preset condition, returning to the step of training the initial residual error attention network and the initial self-encoder by adopting the training data, and/or performing artificial auxiliary evaluation.
8. A risk control device for vehicle insurance claims, comprising:
the data acquisition module is used for acquiring relevant data information of a vehicle to be predicted and external factor data corresponding to the relevant data information;
the evaluation module is used for processing the relevant data information of the vehicle to be predicted and the external factor data of the vehicle to be predicted by adopting a residual attention network, a self-encoder and a neural network to obtain a vehicle loss evaluation index of the vehicle to be predicted;
and the determining module is used for determining a subsequent vehicle insurance claim settlement process according to the vehicle damage evaluation index of the vehicle to be predicted.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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